2 citations · 3 across the 12 of their papers we have counts for
14 papers
ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation
Wenjun Huang, Shenghao Fu, Yian Jin +12
Vision-language-action models for embodied navigation typically rely on RGB observations, limiting their applicability in lights-out industrial environments where visible illuminat…
MERIT: Multi-domain Efficient RAW Image Translation
Wenjun Huang, Shenghao Fu, Yian Jin +10
RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their d…
ObjEmbed: Towards Universal Multimodal Object Embeddings
Shenghao Fu, Yukun Su, Fengyun Rao +3
Aligning objects with corresponding textual descriptions is a fundamental challenge and a realistic requirement in vision-language understanding. While recent multimodal embedding…
WeDetect: Fast Open-Vocabulary Object Detection as Retrieval
Shenghao Fu, Yukun Su, Fengyun Rao +3
Open-vocabulary object detection aims to detect arbitrary classes via text prompts. Methods without cross-modal fusion layers (non-fusion) offer faster inference by treating recogn…
IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation
Yuan-Ming Li, Qize Yang, Nan Lei +5
Recent advances in motion-aware large language models have shown remarkable promise for jointly learning motion understanding and generation knowledge. However, these models typica…
LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in Mechanism via Multi-Step Reasoning
Shenghao Fu, Qize Yang, Yuan-Ming Li +3
Long video understanding is still challenging for recent Large Video-Language Models (LVLMs) due to the conflict between long-form temporal understanding and detailed spatial perce…
HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context
Qize Yang, Shimin Yao, Weixuan Chen +7
With the rapid evolution of multimodal large language models, the capacity to deeply understand and interpret human intentions has emerged as a critical capability, which demands d…
ActionArt: Advancing Multimodal Large Models for Fine-Grained Human-Centric Video Understanding
Yi-Xing Peng, Qize Yang, Yu-Ming Tang +4
Fine-grained understanding of human actions and poses in videos is essential for human-centric AI applications. In this work, we introduce ActionArt, a fine-grained video-caption d…
ViSpeak: Visual Instruction Feedback in Streaming Videos
Shenghao Fu, Qize Yang, Yuan-Ming Li +6
Recent advances in Large Multi-modal Models (LMMs) are primarily focused on offline video understanding. Instead, streaming video understanding poses great challenges to recent mod…
A Hierarchical Semantic Distillation Framework for Open-Vocabulary Object Detection
Shenghao Fu, Junkai Yan, Qize Yang +3
Open-vocabulary object detection (OVD) aims to detect objects beyond the training annotations, where detectors are usually aligned to a pre-trained vision-language model, eg, CLIP,…
LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models
Shenghao Fu, Qize Yang, Qijie Mo +5
Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a…
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding
Jiaxing Zhao, Qize Yang, Yixing Peng +8
In human-centric scenes, the ability to simultaneously understand visual and auditory information is crucial. While recent omni models can process multiple modalities, they general…
Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models
Shenghao Fu, Junkai Yan, Qize Yang +3
Recent vision foundation models can extract universal representations and show impressive abilities in various tasks. However, their application on object detection is largely over…
Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection
Qijie Mo, Yipeng Gao, Shenghao Fu +3
In incremental object detection, knowledge distillation has been proven to be an effective way to alleviate catastrophic forgetting. However, previous works focused on preserving t…